Systems and methods for determining a total amount of carbon emissions of an individual

ABSTRACT

Method and system for determining a total amount of carbon emissions of an individual are disclosed. For example, the method includes collecting, by a computing device, one or more individual telematics data associated with a user during a predetermined time duration, generating, by the computing device, first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data, and estimating, by the computing device, a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 63/000,874, filed Mar. 27, 2020, incorporated by reference herein in its entirety.

FIELD OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to determining a total amount of carbon emissions of an individual. More particularly, certain embodiments of the present disclosure provide systems and methods for determining a total amount of carbon emissions of an individual during a predetermined time duration. Merely by way of example, the present disclosure has been applied to determining a total amount of carbon emissions of an individual during a predetermined time duration based at least in part upon telematics data of the individual collected during the predetermined time duration. But it would be recognized that the present disclosure has much broader range of applicability.

BACKGROUND OF THE DISCLOSURE

Carbon emissions produced by individuals everyday represent a major contributor to climate change. Hence it is highly desirable to develop more accurate techniques for determining a total amount of carbon emissions produced by each individual, which may be shared with the individual.

BRIEF SUMMARY OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to determining a total amount of carbon emissions of an individual. More particularly, certain embodiments of the present disclosure provide methods and systems for determining a total amount of carbon emissions of an individual during a predetermined time duration. Merely by way of example, the present disclosure has been applied to determining a total amount of carbon emissions of an individual during a predetermined time duration based at least in part upon telematics data of the individual collected during the predetermined time duration. But it would be recognized that the present disclosure has much broader range of applicability.

According to some embodiments, a method for determining a total amount of carbon emissions of an individual during a predetermined time duration includes collecting one or more individual telematics data associated with a user during a predetermined time duration. The method further includes generating first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data. Additionally, the method includes estimating a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data.

According to certain embodiments, a computing device for determining a total amount of carbon emissions of an individual during a predetermined time duration includes one or more processors and a memory that stores instructions for execution by the one or more processors. The instructions, when executed, cause the one or more processors to collect one or more individual telematics data associated with a user during a predetermined time duration. Further, the instructions, when executed, cause the one or more processors to generate first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data. Additionally, the instructions, when executed, cause the one or more processors to estimate a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data.

According to some embodiments, a non-transitory computer-readable medium stores instructions for determining a total amount of carbon emissions of an individual during a predetermined time duration. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to collect one or more individual telematics data associated with a user during a predetermined time duration. Further, the non-transitory computer-readable medium includes instructions to generate first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data. Additionally, the non-transitory computer-readable medium includes instructions to estimate a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data.

Depending upon the embodiment, one or more benefits may be achieved. These benefits and various additional objects, features and advantages of the present disclosure can be fully appreciated with reference to the detailed description and accompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified method for determining a total amount of carbon emissions of an individual according to certain embodiments of the present disclosure.

FIGS. 2A and 2B are a simplified method for determining a total amount of carbon emissions of an individual according to some embodiments of the present disclosure.

FIG. 3 is a simplified method for training a first machine learning model according to certain embodiments of the present disclosure.

FIG. 4 is a simplified method for training a second machine learning model according to certain embodiments of the present disclosure.

FIG. 5 is a diagram showing a system for determining a total amount of carbon emissions of an individual according to certain embodiments of the present disclosure.

FIG. 6 is a simplified diagram showing a computing device, according to various embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to determining a total amount of carbon emissions of an individual. More particularly, certain embodiments of the present disclosure provide methods and systems for determining a total amount of carbon emissions of an individual during a predetermined time duration. Merely by way of example, the present disclosure has been applied to determining a total amount of carbon emissions of an individual during a predetermined time duration based at least in part upon telematics data of the individual collected during the predetermined time duration. But it would be recognized that the present disclosure has much broader range of applicability.

I. One or More Methods for Determining a Total Amount of Carbon Emissions of an Individual According to Certain Embodiments

FIG. 1 is a simplified diagram showing a method 100 for determining a total amount of carbon emissions of an individual according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the method 100 is performed by a computing device (e.g., a server 506). However, it should be appreciated that, in some embodiments, some of the method 100 is performed by any computing device (e.g., a mobile device 502).

The method 100 includes process 102 for collecting one or more individual telematics data associated with a user during a predetermined time duration, process 104 for generating lifestyle data based at least in part upon the one or more individual telematics data, and process 106 for estimating a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the lifestyle data.

Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 100 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.

Specifically, at the process 102, the telematics data includes information related to one or more driving behaviors of the user. As an example, the one or more driving behaviors represent a manner in which the user has operated a vehicle. For example, the driving behaviors indicate the user's driving habits and/or driving patterns. Additionally, according to some embodiments, the telematics data further includes information related to one or more places that the user has been. As discussed below, the telematics data is used to determine one or more lifestyle activities engaged by the user according to some embodiments.

According to some embodiments, the telematics data is received, obtained, or otherwise collected from one or more sensors associated with one or more vehicles of the user. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the one or more vehicles. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the one or more drivers) that is connected to the one or more vehicles while the user is operating one of the one or more vehicles. According to certain embodiments, the telematics data is collected continuously or at predetermined time intervals. According to some embodiments, the telematics data is collected based on a triggering event. For example, the telematics data is collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the telematics data may be received, obtained, or otherwise collected from a server (e.g., a server 506) associated with an insurance provider.

At the process 104, the lifestyle data indicates one or more lifestyle activities engaged by the user. The one or more lifestyle activities engaged by the user are determined based at least in part upon the one or more individual telematics data of the user. Since user's lifestyle characteristics affect the user's driving behaviors, certain lifestyle characteristics of the user are predicted based on the user's telematics data. For example, the lifestyle characteristics may include anxiety, hostility, excitement seeking, reckless, aggression, altruism, normlessness, active, cautious, careless, and/or law-abiding. According to some embodiments, the one or more lifestyle activities that are likely to be engaged by the user are predicted based at least in part upon the certain lifestyle characteristics of the user. For example, the lifestyle activities includes activities that generate carbon emissions, such as driving, cooking, and/or turning on air conditioning or heating system. As an example, a user's usage pattern of the air conditioning or heating system of the vehicle (e.g., duration, setting, strength, and/or temperature) is determined based at least in part upon the telematics data of the user. According to some embodiments, the user's usage pattern of the air conditioning or heating system of the vehicle is applied to predict the user's usage of air conditioning and/or heating at home and/or work.

Additionally, as described above, the telematics data may include one or more places that the user has been. In such embodiments, the one or more lifestyle characteristics and the one or more lifestyle activities engaged by the user are determined based on the one or more places that the user has been. According to certain embodiments, the one or more lifestyle characteristics and the one or more lifestyle activities engaged by the user are determined based on sensor data associated with the user and/or received from the user.

At the process 106, the total amount of carbon emissions is carbon emissions predicted to be produced during the one or more lifestyle activities that are likely to be engaged by the user during the predetermined time duration. However, it should be appreciated that, in some embodiments, the total amount of carbon emissions associated with the user during a different time duration may be estimated based on the assumption that the user's lifestyle characteristics do not drastically change.

FIG. 2 is a simplified method for determining a total amount of carbon emissions of an individual according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the method 200 is performed by a computing device (e.g., a server 506). However, it should be appreciated that, in some embodiments, some of the method 200 is performed by any computing device (e.g., a mobile device 502).

The method 200 includes process 202 for collecting one or more individual telematics data associated with a user during a predetermined time duration, process 204 for generating first lifestyle data based at least in part upon the one or more individual telematics data, process 206 for generating, by at least a first model, one or more questions tailored to the user based at least in part upon the one or more individual telematics data, process 208 for presenting the one or more questions to the user, process 210 for receiving, in response to presenting the one or more questions to the user, one or more responses from the user, process 212 for generating second lifestyle data by at least a second model, process 214 for estimating a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data and the second lifestyle data, process 216 for determining whether the total amount of carbon emissions of the user exceeds a predetermined threshold, and process 218 for transmitting a notification to the user indicating that the total amount of carbon emissions of the user exceeded the predetermined threshold.

Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 200 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.

Specifically, at the process 202, the telematics data includes information related to one or more driving behaviors of the user. As an example, the one or more driving behaviors represent a manner in which the user has operated a vehicle. For example, the driving behaviors indicate the user's driving habits and/or driving patterns. Additionally, according to some embodiments, the telematics data further includes information related to one or more places that the user has been. As discussed below, the telematics data is used to determine one or more lifestyle activities engaged by the user according to some embodiments.

According to some embodiments, the telematics data is received, obtained, or otherwise collected from one or more sensors associated with one or more vehicles of the user. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the one or more vehicles. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the one or more drivers) that is connected to the one or more vehicles while the user is operating one of the one or more vehicles. According to certain embodiments, the telematics data is collected continuously or at predetermined time intervals. According to some embodiments, the telematics data is collected based on a triggering event. For example, the telematics data is collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the telematics data may be received, obtained, or otherwise collected from a server (e.g., a server 506) associated with an insurance provider.

At the process 204, the first lifestyle data indicates one or more lifestyle activities engaged by the user. The one or more lifestyle activities engaged by the user are determined based at least in part upon the one or more individual telematics data of the user by using at least the first model. As discussed in detail in FIG. 3 , the first model is trained using general telematics data related to driving behaviors of one or more other users and one or more actual lifestyle activities of the one or more other users. Since user's lifestyle characteristics affect the user's driving behaviors, certain lifestyle characteristics of the user are predicted based on the user's telematics data by using the first model. For example, the lifestyle characteristics may include anxiety, hostility, excitement seeking, reckless, aggression, altruism, normlessness, active, cautious, and/or law-abiding. According to some embodiments, the one or more lifestyle activities that are likely to be engaged by the user are predicted based at least in part upon the certain lifestyle characteristics of the user. For example, the lifestyle activities includes activities that generate carbon emissions, such as driving, cooking, and/or turning on air conditioning or heating system. As an example, a user's usage pattern of the air conditioning or heating system of the vehicle (e.g., duration, setting, strength, and/or temperature) is determined based at least in part upon the telematics data of the user. According to some embodiments, the user's usage pattern of the air conditioning or heating system of the vehicle is applied to predict the user's usage of air conditioning and/or heating at home and/or work.

Additionally, as described above, the telematics data may include one or more places that the user has been. In such embodiments, the one or more lifestyle characteristics and the one or more lifestyle activities engaged by the user are determined based on the one or more places that the user has been. According to certain embodiments, the one or more lifestyle characteristics and the one or more lifestyle activities engaged by the user are determined based on sensor data associated with the user and/or received from the user.

At the process 206, the one or more questions are tailored to the user based at least in part upon the one or more individual telematics data. According to some embodiments, the one or more questions are generated using machine learning. As described above, the telematics data includes information related to one or more driving behaviors of the user, and the driving behaviors are affected by certain lifestyle characteristics of the user. Based on those correlations, one or more questions are generated to gather information related to one or more lifestyle activities engaged by the user.

At the process 208, the one or more questions are presented to the user on a display (e.g., a display 522) of a computing device of a user (e.g., a mobile device 502).

At the process 210, in response to presenting the one or more questions to the user, one or more responses are received from the user. The one or more responses include information related to one or more lifestyle activities engaged bw the user.

At the process 212, the second lifestyle data indicates one or more additional lifestyle activities engaged by the user based at least in part upon the one or more responses.

At the process 214, the total amount of carbon emissions associated with the user is determined using at least the second model based at least in part upon the first lifestyle data and the second lifestyle data. In other words, the total amount of carbon emissions is determined based on the one or more lifestyle activities predicted by the first model and the one or more lifestyle activities indicated by the user.

As discussed in detail in FIG. 4 , the second model is trained using lifestyle activity sensor data related to one or more lifestyle activities and actual carbon emissions associated the one or more lifestyle activities. According to some embodiments, the total amount of carbon emissions is carbon emissions predicted to be produced during the one or more lifestyle activities that are likely to be engaged by the user during the predetermined time duration. However, it should be appreciated that, in certain embodiments, the total amount of carbon emissions associated with the user during a different time duration may be estimated based on the assumption that the user's lifestyle characteristics do not drastically change.

At the process 216, the predetermined threshold may be set by the user according to some embodiments. In certain embodiments, the predetermined threshold may be determined based on user's demographic information (e.g., age, race, ethnicity, gender, marital status, location, and/or employment).

At the process 218, in response to determining that the total amount of carbon emissions of the user exceeds the predetermined threshold, a notification is transmitted to the user indicating that the total amount of carbon emissions of the user exceeded the predetermined threshold. According to some embodiments, the user may choose whether to receive a notification regarding the carbon emissions.

II. One or More Methods for Training Machine Learning Models According to Certain Embodiments

FIG. 3 is a simplified method for training a first machine learning model (also referred to as the first model in this application) for determining one or more lifestyle activities engaged by a user based at least in part upon telematics data of the user according to some embodiments of the present disclosure. As described above, the lifestyle activities engaged by a user relates to lifestyle characteristics of the user, and the lifestyle characteristics of the user may affect driving behaviors (e.g., various driving maneuvers) of the user. For example, the lifestyle characteristics may include anxiety, hostility, excitement seeking, reckless, aggression, altruism, normlessness, active, cautious, and/or law-abiding. As such, the lifestyle characteristics of the user may be predicted based at least in part upon the driving behaviors of the user. As described below, the first machine learning model is trained to determine lifestyle activities of a particular user based upon collected telematics data.

This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. The method 300 includes process 302 for collecting one or more sets of training data for one or more users, process 304 for providing one set of training data to a first machine learning model, process 306 for analyzing the general telematics data of the one set of training data to determine one or more lifestyle characteristics, process 308 for predicting one or more lifestyle activities of the respective user related to the one or more lifestyle characteristics based at least in part upon the one or more driving behaviors, process 310 for comparing the one or more predicted lifestyle activities with the one or more actual lifestyle activities of the respective user, process 312 for adjusting at least one or more parameters of the first machine learning model based at least in part upon the comparison, the one or more parameters being related to the one or more lifestyle characteristics associated with one or more driving behaviors of the respective user, and process 314 for determining whether the training is completed.

Specifically, at the process 302, each set of training data includes general telematics data of a respective user and one or more actual lifestyle activities engaged by the respective user. The general telematics data is related to driving behaviors of the respective user.

At the process 304, the one set of training data includes the general telematics data and the one or more actual lifestyle activities of the respective user. According to some embodiments, the actual lifestyle activities of the respective user are determined based on sensor data associated with the respective user. As an example, the sensor data is collected via one or more wearable device and/or a mobile device of the respective user. Additionally, according to some embodiments, the actual lifestyle activities of the respective user are received from the respective user. For example, one or more questions may be presented to the respective user to inquire actual lifestyle activities that are engaged by the respective user. In response, one or more responses may be received from the respective user indicating one or more actual lifestyle activities engaged by the respective user. As described above, the second machine learning model is trained to determine one or more lifestyle activities of a particular user based upon collected general telematics data.

At the process 306, the general telematics data of the respective user is analyzed to determine one or more lifestyle characteristics of with the user. As described above, the one or more lifestyle characteristics are associated with one or more driving behaviors of the respective user.

At the process 308, the one or more lifestyle activities that are likely to be engaged by the respective user is determined based on the one or more lifestyle characteristics determined at the process 306.

At the process 310, the one or more predicted lifestyle activities that are likely to be engaged by the respective user are compared with the one or more actual lifestyle activities of the respective user.

At the process 312, if the one or more predicted lifestyle activities are different from the one or more actual lifestyle activities of the respective user, at least one or more parameters of the first machine learning model are adjusted. The one or more parameters are related to the one or more lifestyle characteristics associated with one or more driving behaviors of the respective user.

At the process 314, if the training has not been completed, the method 300 loops back to the process 304 to continue training the first machine learning model with another set of training data.

FIG. 4 is a simplified method for training a second machine learning model (also referred to as the second model in this application) for determining predicted carbon emissions produced during one or more lifestyle activities engaged by a user according to some embodiments of the present disclosure. As described above, different lifestyle activities produce different amount of carbon emissions. In the illustrative embodiment, the carbon emissions produced during the lifestyle activities of the user excludes any carbon emissions produced by the user. As described below, the second machine learning model is trained to determine a total amount of carbon emissions of a user during a predetermined time duration based upon the lifestyle activities engaged by the user.

This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. The method 400 includes process 402 for collecting one or more sets of training data for one or more users, process 404 for providing one set of training data to a second machine learning model, process 406 for predicting carbon emissions produced during the lifestyle activity, process 408 for comparing the predicted carbon emissions produced during the lifestyle activity with the actual carbon emissions produced during the lifestyle activity by the respective user, process 410 for adjusting at least at least one or more parameters of the second machine learning model based at least in part upon the comparison, and process 412 for determining whether the training is completed.

Specifically, at the process 402, each set of training data includes a lifestyle activity engaged by a respective user and actual carbon emissions associated with the lifestyle activity of the respective user. According to some embodiments, the lifestyle activity is received from the user. For example, one or more questions may be presented to the respective user to inquire lifestyle activities that are engaged by the respective user. In response, one or more responses may be received from the respective user indicating the one or more lifestyle activities that are engaged by the respective user. According to certain embodiments, the lifestyle activity is determined based on first sensor data associated with the respective user. As an example, the sensor data may be collected via one or more wearable device and/or a mobile device of the respective user.

According to some embodiments, the actual carbon emissions associated with the lifestyle activity of the user is obtained from a third-party source. Additionally or alternatively, in certain embodiments, the actual carbon emissions associated with each lifestyles activity is determined based on second sensor data indicative of carbon emissions. For example, the second sensor data may indicate a fuel consumption of a vehicle or a gas fuel consumption from a gas meter. According to certain embodiments, the second sensor data is obtained from one or more smart sensors that are capable of measuring carbon emissions. Additionally or alternatively, in certain embodiments, the actual carbon emissions associated with each lifestyles activity may be obtained from a third party source. As described above, in the illustrative embodiment, the carbon emissions produced during the lifestyle activity engaged by the respective user excludes any carbon emissions produced by the respective user (e.g., breathing). In other words, the carbon emissions produced during the lifestyle activity engaged by the respective user includes the carbon emissions produced by one or more equipment and/or devices that are used by the respective user during engaging the lifestyle activity. However, it should be appreciated that, in some embodiments, the carbon emissions produced during the lifestyle activity engaged by the user include any carbon emissions produced by the respective user.

At the process 404, the one set of training data related to the lifestyle activity engaged by a respective user and the actual carbon emissions associated the lifestyle activity of the respective user.

At the process 406, the carbon emissions produced during the lifestyle activity engaged by the respective user is predicted.

At the process 408, the predicted amount of carbon emissions produced during the lifestyle activity engaged by the respective user is compared to the actual carbon emissions produced during the lifestyle activity engaged by the respective user.

At the process 410, if the predicted amount of carbon emissions produced during the lifestyle activity engaged by the respective user is different from the actual carbon emissions produced during the lifestyle activity engaged by the respective user, the one or more parameters of the second machine learning model are adjusted. The one or more parameters are related to carbon emissions associated with the lifestyle activity.

At the process 412, if the training has not been completed, the method 400 loops back to the process 404 to continue training the second machine learning model with another set of training data.

III. One or More Systems for Determining a Total Amount of Carbon Emissions of an Individual According to Certain Embodiments

FIG. 5 is a simplified diagram showing a system for determining a total amount of carbon emissions of an individual during a predetermined time duration according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the system 500 includes a mobile device 502, a network 504, and a server 506. Although the above has been shown using a selected group of components for the system, there can be many alternatives, modifications, and variations. For example, some of the components may be expanded and/or combined. Other components may be inserted to those noted above. Depending upon the embodiment, the arrangement of components may be interchanged with others replaced.

In various embodiments, the system 500 is used to implement the method 100 and/or the method 200. According to certain embodiments, the mobile device 502 is communicatively coupled to the server 506 via the network 504. As an example, the mobile device 502 includes one or more processors 516 (e.g., a central processing unit (CPU), a graphics processing unit (GPU)), a memory 518 (e.g., random-access memory (RAM), read-only memory (ROM), flash memory), a communications unit 520 (e.g., a network transceiver), a display unit 522 (e.g., a touchscreen), and one or more sensors 524 (e.g., an accelerometer, a gyroscope, a magnetometer, a location sensor). For example, the one or more sensors 524 are configured to generate the telematics data. According to some embodiments, the telematics data are collected continuously, at predetermined time intervals, and/or based on a triggering event (e.g., when each sensor has acquired a threshold amount of sensor measurements).

In some embodiments, the mobile device 502 is operated by the user. For example, the user installs an application associated with an insurer on the mobile device 502 and allows the application to communicate with the one or more sensors 524 to collect data (e.g., the telematics data). According to some embodiments, the application collects the data continuously, at predetermined time intervals, and/or based on a triggering event (e.g., when each sensor has acquired a threshold amount of sensor measurements). In certain embodiments, the data is used to determine an amount of carbon emissions generated by the user's vehicle in the method 100 and/or the method 200. As an example, the data represents the user's driving behaviors. According to some embodiments, there may be other drivers that drives the user's vehicle. In such embodiments, there may be multiple mobile devices (e.g., mobile devices of one or more drivers of the vehicle) that are in communication with the server 506.

According to certain embodiments, the collected data are stored in the memory 518 before being transmitted to the server 506 using the communications unit 522 via the network 504 (e.g., via a local area network (LAN), a wide area network (WAN), the Internet). In some embodiments, the collected data are transmitted directly to the server 506 via the network 504. In certain embodiments, the collected data are transmitted to the server 506 via a third party. For example, a data monitoring system stores any and all data collected by the one or more sensors 524 and transmits those data to the server 506 via the network 504 or a different network.

According to certain embodiments, the server 506 includes a processor 530 (e.g., a microprocessor, a microcontroller), a memory 532, a communications unit 534 (e.g., a network transceiver), and a data storage 536 (e.g., one or more databases) In some embodiments, the server 506 is a single server, while in certain embodiments, the server 506 includes a plurality of servers with distributed processing. As an example, in FIG. 5 , the data storage 536 is shown to be part of the server 506. In some embodiments, the data storage 536 is a separate entity coupled to the server 506 via a network such as the network 504. In certain embodiments, the server 506 includes various software applications stored in the memory 532 and executable by the processor 530. For example, these software applications include specific programs, routines, or scripts for performing functions associated with the method 100, the method 200, and/or the method 3R). As an example, the software applications include general-purpose software applications for data processing, network communication, database management, web server operation, and/or other functions typically performed by a server.

According to various embodiments, the server 506 receives, via the network 504, the telematics data collected by the one or more sensors 524 from the application using the communications unit 534 and stores the data in the data storage 536. For example, the server 506 then processes the data to perform one or more processes of the method 10, one or more processes of the method 200, and/or one or more processes of the method 300.

According to certain embodiments, the notification in the method 200 is transmitted to the mobile device 502, via the network 504, to be provided (e.g., displayed) to the user via the display unit 522.

In some embodiments, one or more processes of the method 100 and/or one or more processes of the method 200 are performed by the mobile device 502. For example, the processor 516 of the mobile device 502 analyzes the telematics data collected by the one or more sensors 524 to perform one or more processes of the method 100 and/or one or more processes of the method 200.

IV. One or More Computer Devices According to Various Embodiments

FIG. 6 is a simplified diagram showing a computer device 600, according to various embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In some examples, the computer device 600 includes a processing unit 602, a memory unit 604, an input unit 606, an output unit 608, and a communication unit 610. In various examples, the computer device 600 is configured to be in communication with a user 620 and/or a storage device 622. In certain examples, the system computer device 600 is configured according to the system 500 of FIG. 5 to implement the method 100 of FIG. 1 , and/or the method 200 of FIG. 2 . Although the above has been shown using a selected group of components, there can be many alternatives, modifications, and variations. In some examples, some of the components may be expanded and/or combined. Some components may be removed. Other components may be inserted to those noted above. Depending upon the embodiment, the arrangement of components may be interchanged with others replaced.

In various embodiments, the processing unit 602 is configured for executing instructions, such as instructions to implement the method 100 of FIG. 1 and/or the method 200 of FIG. 2 . In some embodiments, executable instructions may be stored in the memory unit 604. In some examples, the processing unit 602 includes one or more processing units (e.g., in a multi-core configuration). In certain examples, the processing unit 602 includes and/or is communicatively coupled to one or more modules for implementing the systems and methods described in the present disclosure. In some examples, the processing unit 602 is configured to execute instructions within one or more operating systems, such as UNIX, LINUX, Microsoft Windows-, etc. In certain examples, upon initiation of a computer-implemented method, one or more instructions is executed during initialization. In some examples, one or more operations is executed to perform one or more processes described herein. In certain examples, an operation may be general or specific to a particular programming language (e.g., C, C#, C++, Java, or other suitable programming languages, etc.). In various examples, the processing unit 602 is configured to be operatively coupled to the storage device 622, such as via an on-board storage unit 612.

In various embodiments, the memory unit 604 includes a device allowing information, such as executable instructions and/or other data to be stored and retrieved. In some examples, the memory unit 604 includes one or more computer readable media. In some embodiments, data stored in the memory unit 604 include computer readable instructions for providing a user interface, such as to the user 604, via the output unit 608. In some examples, a user interface includes a web browser and/or a client application. In various examples, a web browser enables one or more users, such as the user 604, to display and/or interact with media and/or other information embedded on a web page and/or a website. In certain examples, the memory unit 604 include computer readable instructions for receiving and processing an input, such as from the user 604, via the input unit 606. In certain examples, the memory unit 604 includes random access memory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and/or non-volatile RAM (NVRAN).

In various embodiments, the input unit 606 is configured to receive input, such as from the user 604. In some examples, the input unit 606 includes a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touch screen), a gyroscope, an accelerometer, a position detector (e.g., a Global Positioning System), and/or an audio input device. In certain examples, the input unit 606, such as a touch screen of the input unit, is configured to function as both the input unit and the output unit.

In various embodiments, the output unit 608 includes a media output unit configured to present information to the user 604. In some embodiments, the output unit 608 includes any component capable of conveying information to the user 604. In certain embodiments, the output unit 608 includes an output adapter, such as a video adapter and/or an audio adapter. In various examples, the output unit 608, such as an output adapter of the output unit, is operatively coupled to the processing unit 602 and/or operatively coupled to an presenting device configured to present the information to the user, such as via a visual display device (e.g., a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a cathode ray tube (CRT) display, an “electronic ink” display, a projected display, etc.) or an audio display device (e.g., a speaker arrangement or headphones).

In various embodiments, the communication unit 610 is configured to be communicatively coupled to a remote device. In some examples, the communication unit 610 includes a wired network adapter, a wireless network adapter, a wireless data transceiver for use with a mobile phone network (e.g., Global System for Mobile communications (GSM), 3G, 4G, or Bluetooth), and/or other mobile data networks (e.g., Worldwide Interoperability for Microwave Access (WIMAX)). In certain examples, other types of short-range or long-range networks may be used. In some examples, the communication unit 610 is configured to provide email integration for communicating data between a server and one or more clients.

In various embodiments, the storage unit 612 is configured to enable communication between the computer device 600, such as via the processing unit 602, and an external storage device 622. In some examples, the storage unit 612 is a storage interface. In certain examples, the storage interface is any component capable of providing the processing unit 602 with access to the storage device 622. In various examples, the storage unit 612 includes an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any other component capable of providing the processing unit 602 with access to the storage device 622.

In some examples, the storage device 622 includes any computer-operated hardware suitable for storing and/or retrieving data. In certain examples, the storage device 622 is integrated in the computer device 600. In some examples, the storage device 622 includes a database, such as a local database or a cloud database. In certain examples, the storage device 622 includes one or more hard disk drives. In various examples, the storage device is external and is configured to be accessed by a plurality of server systems. In certain examples, the storage device includes multiple storage units such as hard disks or solid-state disks in a redundant array of inexpensive disks (RAID) configuration. In some examples, the storage device 622 includes a storage area network (SAN) and/or a network attached storage (NAS) system.

V. Examples of Machine Learning According to Certain Embodiments

According to some embodiments, a processor or a processing element may be trained using supervised machine learning and/or unsupervised machine learning, and the machine learning may employ an artificial neural network, which, for example, may be a convolutional neural network, a recurrent neural network, a deep learning neural network, a reinforcement learning module or program, or a combined learning module or program that learns in two or more fields or areas of interest. Machine learning may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data models may be created based upon example inputs in order to make valid and reliable predictions for novel inputs.

According to certain embodiments, machine learning programs may be trained by inputting sample data sets or certain data into the programs, such as images, object statistics and information, historical estimates, and/or actual repair costs. The machine learning programs may utilize deep learning algorithms that may be primarily focused on pattern recognition and may be trained after processing multiple examples. The machine learning programs may include Bayesian Program Learning (BPL), voice recognition and synthesis, image or object recognition, optical character recognition, and/or natural language processing. The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or other types of machine learning.

According to some embodiments, supervised machine learning techniques and/or unsupervised machine learning techniques may be used. In supervised machine learning, a processing element may be provided with example inputs and their associated outputs and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output. In unsupervised machine learning, the processing element may need to find its own structure in unlabeled example inputs.

VI. Examples of Certain Embodiments of the Present Disclosure

According to certain embodiments, a method for determining a total amount of carbon emissions of an individual during a predetermined time duration includes collecting one or more individual telematics data associated with a user during a predetermined time duration. The method further includes generating first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data. Additionally, the method includes estimating a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data. For example, the method is implemented according to at least FIG. 1 , FIGS. 2A and 2B, FIG. 3 , and FIG. 4 .

According to certain embodiments, a computing device for determining a total amount of carbon emissions of an individual during a predetermined time duration includes one or more processors and a memory that stores instructions for execution by the one or more processors. The instructions, when executed, cause the one or more processors to collect one or more individual telematics data associated with a user during a predetermined time duration. Further, the instructions, when executed, cause the one or more processors to generate first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data. Additionally, the instructions, when executed, cause the one or more processors to estimate a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data. For example, the computing device (e.g., the server 506) is implemented according to at least FIG. 5 .

According to certain embodiments, a non-transitory computer-readable medium stores instructions for determining a total amount of carbon emissions of an individual during a predetermined time duration. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to collect one or more individual telematics data associated with a user during a predetermined time duration. Further, the non-transitory computer-readable medium includes instructions to generate first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data. Additionally, the non-transitory computer-readable medium includes instructions to estimate a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data. For example, the non-transitory computer-readable medium is implemented according to at least FIG. 1 , FIGS. 2A and 2B, FIG. 3 , and FIG. 4 .

VII. Additional Considerations According to Certain Embodiments

For example, some or all components of various embodiments of the present disclosure each are, individually and/or in combination with at least another component, implemented using one or more software components, one or more hardware components, and/or one or more combinations of software and hardware components. As an example, some or all components of various embodiments of the present disclosure each are, individually and/or in combination with at least another component, implemented in one or more circuits, such as one or more analog circuits and/or one or more digital circuits. For example, while the embodiments described above refer to particular features, the scope of the present disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the described features. As an example, various embodiments and/or examples of the present disclosure can be combined.

Additionally, the methods and systems described herein may be implemented on many different types of processing devices by program code comprising program instructions that are executable by the device processing subsystem. The software program instructions may include source code, object code, machine code, or any other stored data that is operable to cause a processing system to perform the methods and operations described herein. Certain implementations may also be used, however, such as firmware or even appropriately designed hardware configured to perform the methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, data input, data output, intermediate data results, final data results) may be stored and implemented in one or more different types of computer-implemented data stores, such as different types of storage devices and programming constructs (e.g., RAM, ROM. EEPROM, Flash memory, flat files, databases, programming data structures, programming variables. IF-THEN (or similar type) statement constructs, application programming interface). It is noted that data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program.

The systems and methods may be provided on many different types of computer-readable media including computer storage mechanisms (e.g., CD-ROM, diskette, RAM, flash memory, computer's hard drive, DVD) that contain instructions (e.g., software) for use in execution by a processor to perform the methods' operations and implement the systems described herein. The computer components, software modules, functions, data stores and data structures described herein may be connected directly or indirectly to each other in order to allow the flow of data needed for their operations. It is also noted that a module or processor includes a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code. The software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand.

The computing system can include mobile devices and servers. A mobile device and server are generally remote from each other and typically interact through a communication network. The relationship of mobile device and server arises by virtue of computer programs running on the respective computers and having a mobile device-server relationship to each other.

This specification contains many specifics for particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations, one or more features from a combination can in some cases be removed from the combination, and a combination may, for example, be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Although specific embodiments of the present disclosure have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the present disclosure is not to be limited by the specific illustrated embodiments. 

What is claimed is:
 1. A computer-implemented method for determining a total amount of carbon emissions of an individual during a predetermined time duration, the method comprising: collecting, by a computing device, one or more individual telematics data associated with a user during a predetermined time duration: generating, by the computing device, first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data; and estimating, by the computing device, a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data.
 2. The method of claim 1, further comprising: generating, by the computing device, one or more first questions tailored to the user based at least in part upon the one or more individual telematics data; presenting, by the computing device, the one or more first questions to the user; receiving, in response to presenting the one or more first questions to the user and by the computing device, one or more first responses from the user; and generating, by the computing device, second lifestyle data indicative of one or more additional lifestyle activities engaged by the user based at least in part upon the one or more first responses.
 3. The method of claim 2, wherein the estimating the total amount of carbon emissions associated with the user during the predetermined time duration includes estimating the total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data and the second lifestyle data.
 4. The method of claim 1, further comprising: collecting, by the computing device, one or more sets of general telematics data of one or more users; collecting, by the computing device, one or more sets of general lifestyle data associated with the one or more users; and training, by the computing device, a first model using the one or more sets of general telematics data and the one or more sets of general lifestyle data, wherein: the one or more sets of general telematics data indicates driving behaviors of the one or more users, and the one or more sets of general lifestyle data indicates one or more lifestyle activities engaged by the one or more users.
 5. The method of claim 4, wherein the collecting the one or more sets of general lifestyle data associated with the one or more users includes: collecting, by the computing device, sensor data from one or more sensors associated with the one or more users, the sensor data indicative of one or more lifestyle activities engaged by the one or more users; analyzing, by the computing device, the sensor data to select one or more lifestyle activities engaged by the one or more users that produce carbon emissions; providing, by the computing device, one or more second questions to the one or more users; and receiving, in response to providing the one or more second questions and by the computing device, one or more second responses from the one or more users, wherein the one or more sets of general lifestyle data includes the selected lifestyle activities and the one or more second responses.
 6. The method of claim 4, w herein the generating the first lifestyle data indicative of one or more lifestyle activities engaged by the user includes generating, by at least the first model, the first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the individual telematics data.
 7. The method of claim 1, further comprising collecting, by the computing device, one or more lifestyle activity sensor data of the one or more users, the one or more lifestyle activity sensor data indicative of one or more lifestyle activities engaged by the one or more users; collecting, by the computing device, carbon emissions data associated with the one or more lifestyle activities of the one or more users, the carbon emissions data indicative of carbon emissions produced during the one or more lifestyle activities; and training, by the computing device, a second model using the one or more lifestyle activity sensor data and the carbon emissions data.
 8. The method of claim 7, wherein the estimating the total amount of carbon emissions of the individual includes estimating, by at least the second model, the total amount of carbon emissions of the individual during the predefined duration.
 9. The method of claim 1, further comprising: determining, by the computing device, whether the total amount of carbon emissions of the individual exceeds a predetermined threshold; and transmitting, in response to determining that the total amount of carbon emissions of the individual exceeds the predetermined threshold, a notification to the individual indicating that the total amount of carbon emissions of the individual during the predetermined time duration exceeded the predetermined threshold.
 10. A computing device for determining a total amount of carbon emissions of an individual during a predetermined time duration, the computing device comprising: a processor; and a memory having a plurality of instructions stored thereon that, when executed by the processor, causes the computing device to: collect one or more individual telematics data associated with a user during a predetermined time duration; generate first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data; and estimate a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data.
 11. The computing device of claim 10, wherein the plurality of instructions, when executed, further cause the computing device to: generate one or more first questions tailored to the user based at least in part upon the one or more individual telematics data; present the one or more first questions to the user; receive, in response to the one or more first questions being presented to the user, one or more first responses from the user; and generate second lifestyle data indicative of one or more additional lifestyle activities engaged by the user based at least in part upon the one or more first responses.
 12. The computing device of claim 11, wherein the estimating the total amount of carbon emissions associated with the user during the predetermined time duration includes estimating the total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data and the second lifestyle data.
 13. The computing device of claim 10, wherein the plurality of instructions, when executed, further cause the computing device to: collect one or more sets of general telematics data of one or more users, collect one or more sets of general lifestyle data associated with the one or more users; and train a first model using the one or more sets of general telematics data and the one or more sets of general lifestyle data, wherein: the one or more sets of general telematics data indicates one or more driving behaviors of the one or more users, and the one or more sets of general lifestyle data indicates one or more lifestyle activities engaged by the one or more users.
 14. The computing device of claim 13, wherein to collect the one or more sets of general lifestyle data associated with the one or more users includes to: collect sensor data from one or more sensors associated with the one or more users, the sensor data indicative of one or more activities engaged by the one or more users; analyze the sensor data to select one or more lifestyle activities engaged by the one or more users that produce carbon emissions; provide one or more second questions to the one or more users; and receive, in response to the one or more second questions being provided, one or more second responses from the one or more users, wherein the one or more sets of general lifestyle data includes the selected lifestyle activities and the one or more second responses.
 15. The computing device of claim 13, wherein to generate the first lifestyle data indicative of lifestyle activities engaged by the user includes to generate, by at least the first model, the first lifestyle data indicative of lifestyle activities engaged by the user based at least in part upon the individual telematics data.
 16. The computing device of claim 10, wherein the plurality of instructions, when executed, further cause the computing device to: collect one or more lifestyle activity sensor data of the one or more users, the one or more lifestyle activity sensor data indicative of one or more lifestyle activities engaged by the one or more users; collect carbon emissions data associated with the one or more lifestyle activities of the one or more users, the carbon emissions data indicative of carbon emissions produced during the one or more lifestyle activities, and train a second model using the one or more lifestyle activity sensor data and the carbon emissions data.
 17. The computing device of claim 16, wherein to estimate the total individual carbon measures produced by the user includes to estimate, by at least the second model, the total individual carbon measures produced by the user during the predefined duration.
 18. A non-transitory computer-readable medium storing instructions for determining a total amount of carbon emissions of an individual during a predetermined time duration, the instructions when executed by one or more processors of a computing device, cause the computing device to: collect one or more individual telematics data associated with a user during a predetermined time duration; generate first lifestyle data indicative of one or more lifestyle activities engaged by the user based at least in part upon the one or more individual telematics data; and estimate a total amount of carbon emissions associated with the user during the predetermined time duration based at least in part upon the first lifestyle data.
 19. The non-transitory computer-readable medium of claim 18, wherein the instructions when executed by the one or more processors further cause the computing device to: collect one or more sets of general telematics data of one or more users; collect one or more sets of general lifestyle data associated with the one or more users; and train a first model using the one or more sets of general telematics data and the one or more sets of general lifestyle data, wherein the one or more sets of general telematics data indicates driving behaviors of the one or more users, wherein the one or more sets of general lifestyle data indicates one or more lifestyle activities engaged by the one or more users, and wherein to generate the first lifestyle data indicative of one or more lifestyle activities engaged by the user includes to generate, by at least the first model, the first lifestyle data indicative of one or more lifestyle activities engaged by of the user based at least in part upon the individual telematics data.
 20. The non-transitory computer-readable medium of claim 18, wherein the instructions when executed by the one or more processors further cause the computing device to: collect one or more lifestyle activity sensor data of the one or more users, the one or more lifestyle activity sensor data indicative of one or more lifestyle activities engaged by the one or more users; collect carbon emissions data associated with the one or more lifestyle activities of the one or more users, the carbon emissions data indicative of carbon emissions produced during the one or more lifestyle activities; and train a second model using the one or more lifestyle activity sensor data and the carbon emissions data, wherein to estimate the total individual carbon measures produced by the user includes to estimate, by at least the second model, the total individual carbon measures produced by the user during the predefined duration. 